Cargando…
Deep learning-based defects detection of certain aero-engine blades and vanes with DDSC-YOLOv5s
When performed by a person, aero-engine borescope inspection is easily influenced by individual experience and human factors that can lead to incorrect maintenance decisions, potentially resulting in serious disasters, as well as low efficiency. To address the absolute requirements of flight safety...
Autores principales: | Li, Xubo, Wang, Wenqing, Sun, Lihua, Hu, Bin, Zhu, Liang, Zhang, Jincheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9338258/ https://www.ncbi.nlm.nih.gov/pubmed/35906368 http://dx.doi.org/10.1038/s41598-022-17340-7 |
Ejemplares similares
-
Robotic Ultrasonic Testing Technology for Aero-Engine Blades
por: Ma, Pengzhi, et al.
Publicado: (2023) -
A Rapid Method to Achieve Aero-Engine Blade Form Detection
por: Sun, Bin, et al.
Publicado: (2015) -
Aero-Engine Blade Cryogenic Cooling Milling Deformation Simulation and Process Parameter Optimization
por: Chen, Ting, et al.
Publicado: (2023) -
Research on the multi-physics field coupling simulation of aero-rotor blade electrochemical machining
por: Huang, Liang, et al.
Publicado: (2021) -
Image Recognition of Wind Turbine Blade Defects Using Attention-Based MobileNetv1-YOLOv4 and Transfer Learning
por: Zhang, Chen, et al.
Publicado: (2022)